@phdthesis{barabasi02,
 author = {Albert, Reka Zsuzsanna},
 title = {Statistical mechanics of complex networks},
 year = {2001},
 isbn = {0-493-08502-5},
 note = {AAI3000268},
 publisher = {University of Notre Dame},
 address = {Notre Dame, IN, USA},
} 

@article{clauset09,
 author = {Clauset, Aaron and Shalizi, Cosma Rohilla and Newman, M. E. J.},
 title = {Power-Law Distributions in Empirical Data},
 journal = {SIAM Rev.},
 issue_date = {November 2009},
 volume = {51},
 number = {4},
 month = nov,
 year = {2009},
 issn = {0036-1445},
 pages = {661--703},
 numpages = {43},
 url = {http://dx.doi.org/10.1137/070710111},
 doi = {10.1137/070710111},
 acmid = {1655789},
 publisher = {Society for Industrial and Applied Mathematics},
 address = {Philadelphia, PA, USA},
 keywords = {Pareto, Zipf, heavy-tailed distributions, likelihood ratio test, maximum likelihood, model selection, power-law distributions},
}

@inproceedings{crossen02,
 author = {Crossen, Andrew and Budzik, Jay and Hammond, Kristian J.},
 title = {Flytrap: intelligent group music recommendation},
 booktitle = {Proceedings of the 7th international conference on Intelligent user interfaces},
 series = {IUI '02},
 year = {2002},
 isbn = {1-58113-459-2},
 location = {San Francisco, California, USA},
 pages = {184--185},
 numpages = {2},
 url = {http://doi.acm.org/10.1145/502716.502748},
 doi = {10.1145/502716.502748},
 acmid = {502748},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {audio spaces, intelligent environments, ubiquitous computing},
} 

@article{erdos59,
    author = {Erd{\H{o}}s, P. and R{\'{e}}nyi, A.},
    citeulike-article-id = {2547689},
    journal = {Publ. Math. Debrecen},
    keywords = {bibtex-import, network},
    mrnumber = {MR0120167 (22 \#10924)},
    pages = {290--297},
    posted-at = {2008-03-17 20:32:11},
    priority = {2},
    title = {On random graphs. {I}},
    volume = {6},
    year = {1959}
}

@inproceedings{golbek06,
 author = {Golbeck, Jennifer},
 title = {Generating predictive movie recommendations from trust in social networks},
 booktitle = {Proceedings of the 4th international conference on Trust Management},
 series = {iTrust'06},
 year = {2006},
 isbn = {3-540-34295-8, 978-3-540-34295-3},
 location = {Pisa, Italy},
 pages = {93--104},
 numpages = {12},
 url = {http://dx.doi.org/10.1007/11755593_8},
 doi = {10.1007/11755593_8},
 acmid = {2095018},
 publisher = {Springer-Verlag},
 address = {Berlin, Heidelberg},
} 

@article{milgram67,
    author = {Milgram, Stanley},
    citeulike-article-id = {1288399},
    journal = {Psychology Today},
    keywords = {bibtex-import, networks},
    local-url = {milgram/milgram-1967-the small.pdf},
    pages = {60--67},
    posted-at = {2007-05-10 15:25:00},
    priority = {0},
    title = {{The Small World Problem}},
    volume = {2},
    year = {1967}
}

@book{shapiro92,
  title={ENCYCLOPEDIA OF ARTIFICIAL INTELLIGENCE SECOND EDITION},
  author={Sowa, John F. and Shapiro, Stuart C},
  year={1992},
  publisher={New Jersey: A Wiley Interscience Publication}
}

@article{salton75,
 author = {Salton, G. and Wong, A. and Yang, C. S.},
 title = {A vector space model for automatic indexing},
 journal = {Commun. ACM},
 issue_date = {Nov. 1975},
 volume = {18},
 number = {11},
 month = nov,
 year = {1975},
 issn = {0001-0782},
 pages = {613--620},
 numpages = {8},
 url = {http://doi.acm.org/10.1145/361219.361220},
 doi = {10.1145/361219.361220},
 acmid = {361220},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {automatic indexing, automatic information retrieval, content analysis, document space},
} 

@article{steyvers01,
    abstract = {{We present statistical analyses of the large-scale structure of 3 types of semantic networks: word associations, WordNet, and Roget's Thesaurus. We show that they have a small-world structure, characterized by sparse connectivity, short average path lengths between words, and strong local clustering. In addition, the distributions of the number of connections follow power laws that indicate a scale-free pattern of connectivity, with most nodes having relatively few connections joined together through a small number of hubs with many connections. These regularities have also been found in certain other complex natural networks, such as the World Wide Web, but they are not consistent with many conventional models of semantic organization, based on inheritance hierarchies, arbitrarily structured networks, or high-dimensional vector spaces. We propose that these structures reflect the mechanisms by which semantic networks grow. We describe a simple model for semantic growth, in which each new word or concept is connected to an existing network by differentiating the connectivity pattern of an existing node. This model generates appropriate small-world statistics and power-law connectivity distributions, and it also suggests one possible mechanistic basis for the effects of learning history variables (age of acquisition, usage frequency) on behavioral performance in semantic processing tasks.}},
    author = {Steyvers, Mark and Tenenbaum, Joshua B.},
    citeulike-article-id = {3991570},
    citeulike-linkout-0 = {http://dx.doi.org/10.1207/s15516709cog2901\_3},
    doi = {10.1207/s15516709cog2901\_3},
    journal = {Cognitive Science},
    keywords = {learning, semanticnetworks, smallworld, structure, sw},
    number = {1},
    pages = {41--78},
    posted-at = {2009-03-17 16:45:59},
    priority = {3},
    publisher = {Lawrence Erlbaum Associates, Inc.},
    title = {{The Large-Scale Structure of Semantic Networks: Statistical Analyses and a Model of Semantic Growth}},
    url = {http://dx.doi.org/10.1207/s15516709cog2901\_3},
    volume = {29},
    year = {2005}
}

@article{watts98,
    author = {Watts, D. J. and Strogatz, S. H.},
    citeulike-article-id = {1580006},
    journal = {Nature},
    keywords = {phd-draft},
    number = {6684},
    pages = {409--10},
    posted-at = {2007-08-21 13:45:44},
    priority = {2},
    title = {{Collective dynamics of'small-world'networks.}},
    volume = {393},
    year = {1998}
}

@article{lauder98,
    abstract = {{Latent Semantic Analysis (LSA) is a theory and method for extracting and representing the contextual-usage meaning of words by statistical computations applied to a large corpus of text (Landauer and Dumais, 1997). The underlying idea is that the aggregate of all the word contexts in which a given word does and does not appear provides a set of mutual
constraints that largely determines the similarity of meaning of words and sets of words to a variety of ways. For example, its scores overlap those of humans on standard vocabulary
and subject matter tests; it mimics human word sorting and category judgments; it simulates in this issue, it accurately estimates passage coherence, learnability of passages by
individual students, and the quality and quantity of knowledge contained in an essay.}},
    author = {Landauer, Thomas K. and Foltz, Peter W. and Laham, Darrell},
    citeulike-article-id = {2243850},
    journal = {Discourse Processes},
    keywords = {lsa},
    number = {25},
    pages = {259--284},
    posted-at = {2008-01-17 09:01:05},
    priority = {2},
    title = {{An Introduction to Latent Semantic Analysis}},
    year = {1998}
}


